Introduction: MicroRNAs (miRNAs), endogenous small noncoding RNAs regulating the activities of target mRNAs and cellular processes, are present in human plasma in a stable form. In this study, we investigated whether miRNAs are also stably present in synovial fluids and whether plasma and synovial fluid miRNAs could be biomarkers of rheumatoid arthritis (RA) and osteoarthritis (OA). Methods: We measured concentrations of miR-16, miR-132, miR-146a, miR-155 and miR-223 in synovial fluid from patients with RA and OA, and those in plasma from RA, OA and healthy controls (HCs) by quantitative reverse transcription-polymerase chain reaction. Furthermore, miRNAs in the conditioned medium of synovial tissues, monolayer fibroblast-like synoviocytes, and mononuclear cells were examined. Correlations between miRNAs and biomarkers or disease activities of RA were statistically examined. Results: Synovial fluid miRNAs were present and as stable as plasma miRNAs for storage at -20°C and freeze-thawing from -20°C to 4°C. In RA and OA, synovial fluid concentrations of miR-16, miR-132, miR-146a, and miR-223 were significantly lower than their plasma concentrations, and there were no correlation between plasma and synovial fluid miRNAs. Interestingly, synovial tissues, fibroblast-like synoviocytes, and mononuclear cells secreted miRNAs in distinct patterns. The expression patterns of miRNAs in synovial fluid of OA were similar to miRNAs secreted by synovial tissues. Synovial fluid miRNAs of RA were likely to originate from synovial tissues and infiltrating cells. Plasma miR-132 of HC was significantly higher than that of RA or OA with high diagnosability. Synovial fluid concentrations of miR-16, miR-146a miR-155 and miR-223 of RA were significantly higher than those of OA. Plasma miRNAs or ratio of synovial fluid miRNAs to plasma miRNAs, including miR-16 and miR-146a, significantly correlated with tender joint counts and 28-joint Disease Activity Score. Conclusions: Plasma miRNAs had distinct patterns from synovial fluid miRNAs, which appeared to originate from synovial tissue. Plasma miR-132 well differentiated HCs from patients with RA or OA, while synovial fluid miRNAs differentiated RA and OA. Furthermore, plasma miRNAs correlated with the disease activities of RA. Thus, synovial fluid and plasma miRNAs have potential as diagnostic biomarkers for RA and OA and as a tool for the analysis of their pathogenesis.

Staphylococcus aureus is prevalent in surgical site infections (SSI) and leads to death in approximately 1% of patients. Phase IIB/III clinical trial results have demonstrated that vaccination against the iron-regulated surface determinant protein B (IsdB) is associated with an increased mortality rate in patients with SSI. Thus, we hypothesized that S . aureus induces nonneutralizing anti-IsdB antibodies, which facilitate bacterial entry into leukocytes to generate “Trojan horse” leukocytes that disseminate the pathogen. Since hemoglobin (Hb) is the primary target of IsdB, and abundant Hb-haptoglobin (Hb-Hp) complexes in bleeding surgical wounds are normally cleared via CD163-mediated endocytosis by macrophages, we investigated this mechanism in vitro and in vivo. Our results demonstrate that active and passive IsdB immunization of mice renders them susceptible to sepsis following SSI. We also found that a multimolecular complex containing S . aureus protein A–anti-IsdB–IsdB–Hb-Hp mediates CD163-dependent bacterial internalization of macrophages in vitro. Moreover, IsdB-immunized CD163 ^–/– mice are resistant to sepsis following S . aureus SSI, as are normal healthy mice given anti-CD163–neutralizing antibodies. These genetic and biologic CD163 deficiencies did not exacerbate local infection. Thus, anti-IsdB antibodies are a risk factor for S . aureus sepsis following SSI, and disruption of the multimolecular complex and/or CD163 blockade may intervene.

Background and Objectives: Unrestricted kinematic alignment total knee arthroplasty (KA-TKA) with a soft-tissue respecting technique (STRT) is a soft-tissue-dependent tibial resection entailing the restoration of the original soft-tissue tension using ligamentotaxis after resurfacing the femur, based on the concept of restoring the native or pre-osteoarthritis alignment in each patient. However, there is no consensus on the indications of unrestricted KA-TKA with the STRT. We modified the STRT, followed by an investigation of the effects of surgery on the postoperative hip–knee–ankle angle (HKAA). Materials and Methods: We retrospectively analyzed the clinical background data, including the preoperative and postoperative HKAA, of 87 patients who underwent unrestricted KA-TKA with the modified STRT. Univariate and multivariate analyses were performed to determine the factors affecting the postoperative HKAA. A receiver operating characteristic (ROC) curve was plotted to investigate the change in the cut-off values of preoperative HKAA with respect to the safe zone of the postoperative HKAA. We generated two regression models, the linear regression model and generalized additive model (GAM) using machine learning, to predict the postoperative HKAA. Results: Univariate and multivariate analyses revealed the preoperative HKAA as the factor most relevant to the postoperative HKAA. ROC analysis revealed that the preoperative HKAA exhibited a high predictive utility, with a cut-off value of −10°, when the safe range of postoperative HKAA was set at ±5°. The GAM was the superior machine learning model, indicating a non-linear association between the preoperative and postoperative HKAA. Patients with preoperative HKAAs ranging from −18° to 4° were more likely to fall within the ±5° safe range of the postoperative HKAA. Conclusions: The preoperative HKAA influences the postoperative HKAA in unrestricted KA-TKA with the modified STRT. Machine learning using the GAM may contribute to the selection of patients eligible for the surgical approach.

Background and objectives: Unrestricted kinematic alignment total knee arthroplasty (KA-TKA) with a soft tissue respecting technique (STRT) is a soft tissue-dependent tibial resection entailing restoration of the original soft tissue tension using ligamentotaxis after resurfacing the femur, based on the concept of restoring the native or pre-osteoarthritis alignment in each patient. However, there is no consensus on the indications of unrestricted KA-TKA with STRT. We modified the STRT, followed by investigation of the effects of surgery on the postoperative hip-knee-ankle angle (HKAA). Materials and Methods: We retrospectively analyzed the clinical background data, including the preoperative and postoperative HKAA, of 87 patients who underwent unrestricted KA-TKA with the modified STRT. Univariate and multivariate analyses were performed to determine the factors affecting the postoperative HKAA. A receiver operating characteristic (ROC) curve was plot-ted to investigate the change in the cut-off values of preoperative HKAA with respect to the safe zone of the postoperative HKAA. We generated two regression models, the linear regression model and generalized additive model (GAM) using machine learning, to predict the postopera-tive HKAA. Results: Univariate and multivariate analyses revealed the preoperative HKAA as the factor most relevant to the postoperative HKAA. ROC analysis revealed that the preoperative HKAA exhibited high predictive utility, with a cut-off value of -10°, when the safe range of postoperative HKAA was set at ± 5°. The GAM was the superior machine learning model, indicating a non-linear association between the preoperative and postoperative HKAA. Patients with preoperative HKAAs ranging from -18° to 4° were more likely to fall within the ± 5° safe range of the postoperative HKAA. Conclusions: The preoperative HKAA influences the postoperative HKAA in unrestricted KA-TKA with the modified STRT. Machine learning using the GAM may contribute to the selection of patients eligible for the surgical approach.

Restricted kinematically aligned total knee arthroplasty (rKA-TKA) for severe deformity in the preoperative hip–knee–ankle angle (HKAA) has gained considerable interest. However, the widespread adoption of rKA-TKA has been limited by its requirement for expensive equipment such as navigation and robotic surgery systems. In this study, we developed manual rKA-TKA with modified soft tissue-respecting technique and investigated its surgical effects on postoperative HKAA. To achieve this, we examined factors affecting postoperative HKAA. Subsequently, the safe zone-related cut-off values of preoperative HKAA were calculated using the receiver operating characteristic curve, and postoperative HKAA was predicted using a linear regression model (LRM) and generalized additive model (GAM). Preoperative HKAA was identified as a factor influencing postoperative HKAA. The cut-off values of preoperative HKAA were −14 and −15° when the safe zones were defined as ± 1–3 and ± 4–5°, respectively. The GAM was more accurate in predicting the postoperative HKAA than the LRM. Additionally, the GAM showed a potential of falling within ± 5° of the postoperative HKAA, even in patients with preoperative HKAA ≤ − 19°. These findings suggest that manual rKA-TKA can be effective even for patients with severe deformities, providing an accessible alternative to conventional TKA for surgeons at resource-limited institutions.